First, the parameter setting procedure used in this study could be refined to avoid optimization bottlenecks and limitations that affect all layerwise training protocols at scale. In particular, the Powell method while effective at small \(N\)  would eventually become intractable, and alternative gradient methods might be hampered by barren plateaus if the parameter setting protocol is kept to be “layerwise” [1]}. Recently developed analytical methods based on series expansion  [2]} or quantum control  [3]} methods might come in handy to analyze further the reachability deficits and strengths of these algorithms [4]}, study the observed optimal parameter concentration [5]}, and to estimate the performance of QAMPA at scale.
Ties between quantum annealing schedule and QAOA parameter setting [6]}, [7]}, [8]},
further indicating that cross-overs between digital and analog optimization methods are also an interesting possible development for QAMPA [9]}, [10]}, [11]}, [12]}.
